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Sensing Attribute Weights: A Novel Basic Belief Assignment Method

Dempster–Shafer evidence theory is widely used in many soft sensors data fusion systems on account of its good performance for handling the uncertainty information of soft sensors. However, how to determine basic belief assignment (BBA) is still an open issue. The existing methods to determine BBA d...

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Detalles Bibliográficos
Autores principales: Jiang, Wen, Zhuang, Miaoyan, Xie, Chunhe, Wu, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5421681/
https://www.ncbi.nlm.nih.gov/pubmed/28358325
http://dx.doi.org/10.3390/s17040721
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author Jiang, Wen
Zhuang, Miaoyan
Xie, Chunhe
Wu, Jun
author_facet Jiang, Wen
Zhuang, Miaoyan
Xie, Chunhe
Wu, Jun
author_sort Jiang, Wen
collection PubMed
description Dempster–Shafer evidence theory is widely used in many soft sensors data fusion systems on account of its good performance for handling the uncertainty information of soft sensors. However, how to determine basic belief assignment (BBA) is still an open issue. The existing methods to determine BBA do not consider the reliability of each attribute; at the same time, they cannot effectively determine BBA in the open world. In this paper, based on attribute weights, a novel method to determine BBA is proposed not only in the closed world, but also in the open world. The Gaussian model of each attribute is built using the training samples firstly. Second, the similarity between the test sample and the attribute model is measured based on the Gaussian membership functions. Then, the attribute weights are generated using the overlap degree among the classes. Finally, BBA is determined according to the sensed attribute weights. Several examples with small datasets show the validity of the proposed method.
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spelling pubmed-54216812017-05-12 Sensing Attribute Weights: A Novel Basic Belief Assignment Method Jiang, Wen Zhuang, Miaoyan Xie, Chunhe Wu, Jun Sensors (Basel) Article Dempster–Shafer evidence theory is widely used in many soft sensors data fusion systems on account of its good performance for handling the uncertainty information of soft sensors. However, how to determine basic belief assignment (BBA) is still an open issue. The existing methods to determine BBA do not consider the reliability of each attribute; at the same time, they cannot effectively determine BBA in the open world. In this paper, based on attribute weights, a novel method to determine BBA is proposed not only in the closed world, but also in the open world. The Gaussian model of each attribute is built using the training samples firstly. Second, the similarity between the test sample and the attribute model is measured based on the Gaussian membership functions. Then, the attribute weights are generated using the overlap degree among the classes. Finally, BBA is determined according to the sensed attribute weights. Several examples with small datasets show the validity of the proposed method. MDPI 2017-03-30 /pmc/articles/PMC5421681/ /pubmed/28358325 http://dx.doi.org/10.3390/s17040721 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jiang, Wen
Zhuang, Miaoyan
Xie, Chunhe
Wu, Jun
Sensing Attribute Weights: A Novel Basic Belief Assignment Method
title Sensing Attribute Weights: A Novel Basic Belief Assignment Method
title_full Sensing Attribute Weights: A Novel Basic Belief Assignment Method
title_fullStr Sensing Attribute Weights: A Novel Basic Belief Assignment Method
title_full_unstemmed Sensing Attribute Weights: A Novel Basic Belief Assignment Method
title_short Sensing Attribute Weights: A Novel Basic Belief Assignment Method
title_sort sensing attribute weights: a novel basic belief assignment method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5421681/
https://www.ncbi.nlm.nih.gov/pubmed/28358325
http://dx.doi.org/10.3390/s17040721
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